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This paper introduces an integer optimization model to optimize wildfire suppression by jointly determining crew assignments and wildfire suppression strategies. The model incorporates a time-space-rest network for crew assignments, a time-state network for wildfire dynamics, and linking constraints between them. To solve the model, the authors develop a two-sided branch-and-price-and-cut algorithm and a data-driven double machine learning approach to estimate wildfire spread, demonstrating significant reductions in burned area in real-world instances.
Optimizing wildfire suppression via integer programming and machine learning can significantly reduce burned areas and improve resource allocation, offering a data-driven approach to a critical real-world problem.
Intense wildfire seasons require critical prioritization decisions to allocate scarce suppression resources over a dispersed geographical area. This paper develops a predictive and prescriptive approach to jointly optimize crew assignments and wildfire suppression. The problem features a discrete resource-allocation structure with endogenous wildfire demand and non-linear wildfire dynamics. We formulate an integer optimization model with crew assignments on a time-space-rest network, wildfire dynamics on a time-state network, and linking constraints between them. We develop a two-sided branch-and-price-and-cut algorithm based on: (i) a two-sided column generation scheme that generates fire suppression plans and crew routes iteratively; (ii) a new family of cuts exploiting the knapsack structure of the linking constraints; and (iii) novel branching rules to accommodate non-linear wildfire dynamics. We also propose a data-driven double machine learning approach to estimate wildfire spread as a function of covariate information and suppression efforts, mitigating observed confounding between historical crew assignments and wildfire growth. Extensive computational experiments show that the optimization algorithm scales to otherwise intractable real-world instances; and that the methodology can enhance suppression effectiveness in practice, resulting in significant reductions in area burned over a wildfire season and guiding resource sharing across wildfire jurisdictions.
